{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:Q33W2G3XJCN7LJJ7PHK7K7JLQK","short_pith_number":"pith:Q33W2G3X","canonical_record":{"source":{"id":"1608.00027","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-07-29T20:57:06Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"99e9c57d0cd0bdb2418602663cc31b05282cfe56274f9be46567c7d4d97aca9c","abstract_canon_sha256":"be5379fa5f51fe1224e3ae5027da233a4dd5b288188a0078c55a2846778bcfad"},"schema_version":"1.0"},"canonical_sha256":"86f76d1b77489bf5a53f79d5f57d2b82a5876a397b857ad6a416bcc2a5166230","source":{"kind":"arxiv","id":"1608.00027","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.00027","created_at":"2026-05-18T01:10:13Z"},{"alias_kind":"arxiv_version","alias_value":"1608.00027v1","created_at":"2026-05-18T01:10:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.00027","created_at":"2026-05-18T01:10:13Z"},{"alias_kind":"pith_short_12","alias_value":"Q33W2G3XJCN7","created_at":"2026-05-18T12:30:39Z"},{"alias_kind":"pith_short_16","alias_value":"Q33W2G3XJCN7LJJ7","created_at":"2026-05-18T12:30:39Z"},{"alias_kind":"pith_short_8","alias_value":"Q33W2G3X","created_at":"2026-05-18T12:30:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:Q33W2G3XJCN7LJJ7PHK7K7JLQK","target":"record","payload":{"canonical_record":{"source":{"id":"1608.00027","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-07-29T20:57:06Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"99e9c57d0cd0bdb2418602663cc31b05282cfe56274f9be46567c7d4d97aca9c","abstract_canon_sha256":"be5379fa5f51fe1224e3ae5027da233a4dd5b288188a0078c55a2846778bcfad"},"schema_version":"1.0"},"canonical_sha256":"86f76d1b77489bf5a53f79d5f57d2b82a5876a397b857ad6a416bcc2a5166230","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:10:13.973660Z","signature_b64":"0XBvalXuSnxWn2uppiHJDKY0XzNZKYtGMFIjsOZK60YKSZTBrn1qtsXZnUdeM977JexTPUpiZhxDbwAxWIJKDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"86f76d1b77489bf5a53f79d5f57d2b82a5876a397b857ad6a416bcc2a5166230","last_reissued_at":"2026-05-18T01:10:13.973255Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:10:13.973255Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1608.00027","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:10:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NE8B6X3qTB69dt/mWVI3YMm2/oIEXKORjOEwOgP3ga3Da3DCUtVNvcD2wLpgS54xrcru4cv7u/X9m+ZfaHHkCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T14:39:14.356517Z"},"content_sha256":"58dc980e5aaeda815e648e9fdc6c909b4309dfc00b25e55fe30a3dfab9c7a216","schema_version":"1.0","event_id":"sha256:58dc980e5aaeda815e648e9fdc6c909b4309dfc00b25e55fe30a3dfab9c7a216"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:Q33W2G3XJCN7LJJ7PHK7K7JLQK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"gLOP: the global and Local Penalty for Capturing Predictive Heterogeneity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Daniel J. Lizotte, Rhiannon V. Rose","submitted_at":"2016-07-29T20:57:06Z","abstract_excerpt":"When faced with a supervised learning problem, we hope to have rich enough data to build a model that predicts future instances well. However, in practice, problems can exhibit predictive heterogeneity: most instances might be relatively easy to predict, while others might be predictive outliers for which a model trained on the entire dataset does not perform well. Identifying these can help focus future data collection. We present gLOP, the global and Local Penalty, a framework for capturing predictive heterogeneity and identifying predictive outliers. gLOP is based on penalized regression fo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.00027","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:10:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FJUrA5Bg1hBnSQVR9JBcoqwYLqCjVzPwz5gNPQM4+pXn+2jl9YeD00+zwSMsAlYI+HXbjPhaVGCJ0zMKBzvnAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T14:39:14.356862Z"},"content_sha256":"226cf997e8b815b9347fc4dedbd88be86c845c1e7593a3cb9b05faece7792377","schema_version":"1.0","event_id":"sha256:226cf997e8b815b9347fc4dedbd88be86c845c1e7593a3cb9b05faece7792377"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Q33W2G3XJCN7LJJ7PHK7K7JLQK/bundle.json","state_url":"https://pith.science/pith/Q33W2G3XJCN7LJJ7PHK7K7JLQK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Q33W2G3XJCN7LJJ7PHK7K7JLQK/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-07T14:39:14Z","links":{"resolver":"https://pith.science/pith/Q33W2G3XJCN7LJJ7PHK7K7JLQK","bundle":"https://pith.science/pith/Q33W2G3XJCN7LJJ7PHK7K7JLQK/bundle.json","state":"https://pith.science/pith/Q33W2G3XJCN7LJJ7PHK7K7JLQK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Q33W2G3XJCN7LJJ7PHK7K7JLQK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:Q33W2G3XJCN7LJJ7PHK7K7JLQK","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"be5379fa5f51fe1224e3ae5027da233a4dd5b288188a0078c55a2846778bcfad","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-07-29T20:57:06Z","title_canon_sha256":"99e9c57d0cd0bdb2418602663cc31b05282cfe56274f9be46567c7d4d97aca9c"},"schema_version":"1.0","source":{"id":"1608.00027","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.00027","created_at":"2026-05-18T01:10:13Z"},{"alias_kind":"arxiv_version","alias_value":"1608.00027v1","created_at":"2026-05-18T01:10:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.00027","created_at":"2026-05-18T01:10:13Z"},{"alias_kind":"pith_short_12","alias_value":"Q33W2G3XJCN7","created_at":"2026-05-18T12:30:39Z"},{"alias_kind":"pith_short_16","alias_value":"Q33W2G3XJCN7LJJ7","created_at":"2026-05-18T12:30:39Z"},{"alias_kind":"pith_short_8","alias_value":"Q33W2G3X","created_at":"2026-05-18T12:30:39Z"}],"graph_snapshots":[{"event_id":"sha256:226cf997e8b815b9347fc4dedbd88be86c845c1e7593a3cb9b05faece7792377","target":"graph","created_at":"2026-05-18T01:10:13Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"When faced with a supervised learning problem, we hope to have rich enough data to build a model that predicts future instances well. However, in practice, problems can exhibit predictive heterogeneity: most instances might be relatively easy to predict, while others might be predictive outliers for which a model trained on the entire dataset does not perform well. Identifying these can help focus future data collection. We present gLOP, the global and Local Penalty, a framework for capturing predictive heterogeneity and identifying predictive outliers. gLOP is based on penalized regression fo","authors_text":"Daniel J. Lizotte, Rhiannon V. Rose","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-07-29T20:57:06Z","title":"gLOP: the global and Local Penalty for Capturing Predictive Heterogeneity"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.00027","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:58dc980e5aaeda815e648e9fdc6c909b4309dfc00b25e55fe30a3dfab9c7a216","target":"record","created_at":"2026-05-18T01:10:13Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"be5379fa5f51fe1224e3ae5027da233a4dd5b288188a0078c55a2846778bcfad","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-07-29T20:57:06Z","title_canon_sha256":"99e9c57d0cd0bdb2418602663cc31b05282cfe56274f9be46567c7d4d97aca9c"},"schema_version":"1.0","source":{"id":"1608.00027","kind":"arxiv","version":1}},"canonical_sha256":"86f76d1b77489bf5a53f79d5f57d2b82a5876a397b857ad6a416bcc2a5166230","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"86f76d1b77489bf5a53f79d5f57d2b82a5876a397b857ad6a416bcc2a5166230","first_computed_at":"2026-05-18T01:10:13.973255Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:10:13.973255Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0XBvalXuSnxWn2uppiHJDKY0XzNZKYtGMFIjsOZK60YKSZTBrn1qtsXZnUdeM977JexTPUpiZhxDbwAxWIJKDg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:10:13.973660Z","signed_message":"canonical_sha256_bytes"},"source_id":"1608.00027","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:58dc980e5aaeda815e648e9fdc6c909b4309dfc00b25e55fe30a3dfab9c7a216","sha256:226cf997e8b815b9347fc4dedbd88be86c845c1e7593a3cb9b05faece7792377"],"state_sha256":"7767f3e50004c63bb384666a8f937c3c3a5808494db64cf0032b366f15f6e793"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5kY7cPCft/U6F+iaPO9tVxc3awh5dJXH8+P2UwIV6J8Lyu2+imH8V4a2v7Xm2ojZJbIb8d4/eokahIY69xYdBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T14:39:14.358665Z","bundle_sha256":"0471b446423a8aa33ca28dfefadb3464a72354af6bd124336dd4a1ae159d0556"}}